Jurnal Ilmiah Komputer dan Informatika KOMPUTA
5
Edisi. .. Volume. .., Bulan 20.. ISSN : 2089-9033
tokenizing process is carried out so that the resulting separation of each word.
2.4 Implementation of Naïve Bayes Algorithm
Stage In this stage, Naïve Bayes algorithm analysis process which is important in the
classification of the sources of data on its sentiment is positive or negative. In this phase there are two main
processes to do the classification is the process of learning and classification process. The following is
an explanation of each process. 1. Learing Process
In this process naïve Bayes classifier needs to be given prior knowledge to be used as a reference in
order to perform the classification of the textual data based on sentiments. In the process of
teaching or learning, there are three main steps. Here are the three main steps including its
explanation. a.
Determination of Data Class Practice At this stage, the determination of the class of
data. Determination of the class is determined with the help of users by providing an opinion
on whether the search keywords included in the positive class or negative class. Here is an
example of the determination of class training data are presented on Tabel 5.
Tabel 5 Determining The Data Class
Data Word
Sentiment Class
D1 food
people example
Positif D2
kind of
cat Positif
D3 how
to avoid
violence Positif
D4 how
to bully
people Negatif
D5 example
of violence
Negatif D6
good violence
Negatif b. Probability
At this stage, probability calculations on the data that has been determined class. Tabel 6
the calculation of the probability of each class. Tabel 6 Probability Accounting
Sentime nt class
Count glasses Probability
D 1
D 2
D 3
D 4
D 5
D 6
Positif 3
3 4
1019 Negatif
4 3
2 919
Total 3
3 4
4 3
2 1
c. Determining The Probability of a Item Once the probability of each class is
calculated, then calculated the probability of each item. Here is the formula to calculate the
probability per-item.
✄
p i = Probability item f i = Frequency item
f c = The total number of items based on class sentiments.
The following is a calculation of the probability of each item presented on Tabel 7.
Tabel 7 Count Item robability
Data Sentiment Class
Positive Negative
good
☎ ☎ ✆
☎ ✝
people
☎ ☎ ✆
☎ ✝
example
☎ ☎ ✆
- kind
☎ ☎ ✆
- of
☎ ☎ ✆
☎ ✝
cats
☎ ☎ ✆
- how
☎ ☎ ✆
☎ ✝
do
☎ ☎ ✆
☎ ✝
avoid
☎ ☎ ✆
- violence
☎ ☎ ✆
✞ ✝
bully -
☎ ✝
people -
☎ ✝
2. Classification Process In this phase will be the classification of the
new data, namely as test data using naïve Bayes classifier. Here is a plot of the classification process
which can be seen in Gambar 5.
